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      Comparing the Diagnostic Performance of Contrast-Enhanced Mammography and Breast MRI: a Systematic Review and Meta-Analysis

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          Abstract

          Background: To provide a systematic review and meta-analysis that evaluates the diagnostic accuracy of contrast-enhanced mammography (CEM) compared to standard contrast-enhanced breast magnetic resonance imaging (breast MRI). Like breast MRI, CEM enables tumour visualization by contrast accumulation. CEM seems to be a viable substitute for breast MRI.

          Methods: This systematic search assessed the diagnostic accuracy of these techniques in women with suspicious breast lesions on prior imaging or physical examination, who have undergone both breast MRI and CEM. CEM had to be performed on a commercially available system. The MRI sequence parameters had to be described sufficiently to ensure that standard breast MRI sequence protocols were used. Pooled values of sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, and diagnostic odds ratio (DOR), were estimated using bivariate mixed-effects logistic regression modeling. Hierarchical summary receiver operating characteristic curves for CEM and breast MRI were also constructed.

          Results: Six studies (607 patients with 775 lesions) met the predefined inclusion criteria. Pooled sensitivity was 96% for CEM and 97% for breast MRI. Pooled specificity was 77% for both modalities. DOR was 79.5 for CEM and 122.9 for breast MRI. Between-study heterogeneity expressed as the I 2 -index was substantial with values over 80%.

          Conclusion: Pooled sensitivity was high for both CEM and breast MRI, with moderate specificity. The pooled DOR estimates, however, indicate higher overall diagnostic performance of breast MRI compared to CEM. Nonetheless, current scientific evidence is too limited to prematurely discard CEM as an alternative for breast MRI.

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          Most cited references47

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          Quantifying heterogeneity in a meta-analysis.

          The extent of heterogeneity in a meta-analysis partly determines the difficulty in drawing overall conclusions. This extent may be measured by estimating a between-study variance, but interpretation is then specific to a particular treatment effect metric. A test for the existence of heterogeneity exists, but depends on the number of studies in the meta-analysis. We develop measures of the impact of heterogeneity on a meta-analysis, from mathematical criteria, that are independent of the number of studies and the treatment effect metric. We derive and propose three suitable statistics: H is the square root of the chi2 heterogeneity statistic divided by its degrees of freedom; R is the ratio of the standard error of the underlying mean from a random effects meta-analysis to the standard error of a fixed effect meta-analytic estimate, and I2 is a transformation of (H) that describes the proportion of total variation in study estimates that is due to heterogeneity. We discuss interpretation, interval estimates and other properties of these measures and examine them in five example data sets showing different amounts of heterogeneity. We conclude that H and I2, which can usually be calculated for published meta-analyses, are particularly useful summaries of the impact of heterogeneity. One or both should be presented in published meta-analyses in preference to the test for heterogeneity. Copyright 2002 John Wiley & Sons, Ltd.
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            QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies.

            In 2003, the QUADAS tool for systematic reviews of diagnostic accuracy studies was developed. Experience, anecdotal reports, and feedback suggested areas for improvement; therefore, QUADAS-2 was developed. This tool comprises 4 domains: patient selection, index test, reference standard, and flow and timing. Each domain is assessed in terms of risk of bias, and the first 3 domains are also assessed in terms of concerns regarding applicability. Signalling questions are included to help judge risk of bias. The QUADAS-2 tool is applied in 4 phases: summarize the review question, tailor the tool and produce review-specific guidance, construct a flow diagram for the primary study, and judge bias and applicability. This tool will allow for more transparent rating of bias and applicability of primary diagnostic accuracy studies.
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              Bivariate analysis of sensitivity and specificity produces informative summary measures in diagnostic reviews.

              Studies of diagnostic accuracy most often report pairs of sensitivity and specificity. We demonstrate the advantage of using bivariate meta-regression models to analyze such data. We discuss the methodology of both the summary Receiver Operating Characteristic (sROC) and the bivariate approach by reanalyzing the data of a published meta-analysis. The sROC approach is the standard method for meta-analyzing diagnostic studies reporting pairs of sensitivity and specificity. This method uses the diagnostic odds ratio as the main outcome measure, which removes the effect of a possible threshold but at the same time loses relevant clinical information about test performance. The bivariate approach preserves the two-dimensional nature of the original data. Pairs of sensitivity and specificity are jointly analyzed, incorporating any correlation that might exist between these two measures using a random effects approach. Explanatory variables can be added to the bivariate model and lead to separate effects on sensitivity and specificity, rather than a net effect on the odds ratio scale as in the sROC approach. The statistical properties of the bivariate model are sound and flexible. The bivariate model can be seen as an improvement and extension of the traditional sROC approach.
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                Author and article information

                Journal
                J Cancer
                J Cancer
                jca
                Journal of Cancer
                Ivyspring International Publisher (Sydney )
                1837-9664
                2023
                1 January 2023
                : 14
                : 1
                : 174-182
                Affiliations
                [1 ]GROW School for Oncology and Reproduction, Maastricht University, Universiteitssingel 40, 6229 ER Maastricht, the Netherlands.
                [2 ]Department of Radiology and Nuclear Medicine, Maastricht University Medical Center+, P. Debyelaan 25, 6229 HX Maastricht, the Netherlands.
                [3 ]Department of Radiology, Memorial Sloan Kettering Cancer Center, 1275 York Avenue, New York, NY 10065, USA.
                [4 ]Department of Medical Imaging, Laurentius hospital, Mgr. Driessenstrtaat 6, 6040AX Roermond, the Netherlands.
                [5 ]Department of Surgery, Maastricht University Medical Center+, P. Debyelaan 25, 6229 HX Maastricht, the Netherlands.
                [6 ]Department of Epidemiology, Maastricht University, P. Debyelaan 1, 6229 HA Maastricht, the Netherlands.
                [7 ]Department of Medical Imaging, Zuyderland Medical Center, Dr. H. van der Hoffplein 1, 6162 BG Sittard-Geleen, the Netherlands.
                Author notes
                ✉ Corresponding author: E-mail: lidewij.neeter@ 123456mumc.nl .

                Competing Interests: MJ received a speaker's fee from GE Healthcare.

                JW received institutional grants from AGFA, Bayer Healthcare, Bard Medical, GE Healthcare, Optimed, Philips Healthcare, Siemens Healthineers.

                ML received a research grant and several speaker's fees from GE Healthcare, Tromp Medical, Bayer and Guerbet.

                The other authors have declared that no competing interests exist.

                Article
                jcav14p0174
                10.7150/jca.79747
                9809339
                36605487
                4e3c9438-abd7-4ae3-9333-0793c957d2f3
                © The author(s)

                This is an open access article distributed under the terms of the Creative Commons Attribution License ( https://creativecommons.org/licenses/by/4.0/). See http://ivyspring.com/terms for full terms and conditions.

                History
                : 11 October 2022
                : 3 December 2022
                Categories
                Review

                Oncology & Radiotherapy
                contrast-enhanced mammography,breast mri,diagnostic accuracy
                Oncology & Radiotherapy
                contrast-enhanced mammography, breast mri, diagnostic accuracy

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